app.py

# import streamlit as st
# from openai import OpenAI
# import os
# import sys
# from dotenv import load_dotenv, dotenv_values
# load_dotenv()



# # initialize the client
# client = OpenAI(
#   base_url="https://api-inference.huggingface.co/v1",
#   api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN')#"hf_xxx" # Replace with your token
# ) 



# #Create supported models
# model_links ={
#     "Mistral-7b":"mistralai/Mistral-7B-Instruct-v0.2",
#     "Mistral-8x7b":"mistralai/Mixtral-8x7B-Instruct-v0.1" 
#     # "Gemma-7B":"google/gemma-7b-it",
#     # "Gemma-2B":"google/gemma-2b-it",
#     # "Zephyr-7B-β":"HuggingFaceH4/zephyr-7b-beta",

# }

# #Pull info about the model to display
# model_info ={
#     "Mistral-7b":
#         {'description':"""The Mistral 7B model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
#             \nIt was created by the [**Mistral AI**](https://mistral.ai/news/announcing-mistral-7b/) team as has over  **7 billion parameters.** \n""",
#         'logo':'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'},

#         "Mistral-8x7b":
#         {'description':"""The Mistral 8x7B model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
#             \nIt was created by the [**Mistral AI**](https://mistral.ai/news/announcing-mistral-8x7b/) team as has based on MOE arch.** \n""",
#         'logo':'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'},

    
#     # "Gemma-7B":        
#     #     {'description':"""The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
#     #         \nIt was created by the [**Google's AI Team**](https://blog.google/technology/developers/gemma-open-models/) team as has over  **7 billion parameters.** \n""",
#     #     'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'},
#     # "Gemma-2B":        
#     # {'description':"""The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
#     #     \nIt was created by the [**Google's AI Team**](https://blog.google/technology/developers/gemma-open-models/) team as has over  **2 billion parameters.** \n""",
#     # 'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'},
#     # "Zephyr-7B":        
#     # {'description':"""The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
#     #     \nFrom Huggingface: \n\
#     #     Zephyr is a series of language models that are trained to act as helpful assistants. \
#     #     [Zephyr 7B Gemma](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)\
#     #     is the third model in the series, and is a fine-tuned version of google/gemma-7b \
#     #     that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""",
#     # 'logo':'https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/resolve/main/thumbnail.png'},
#     # "Zephyr-7B-β":        
#     # {'description':"""The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
#     #     \nFrom Huggingface: \n\
#     #     Zephyr is a series of language models that are trained to act as helpful assistants. \
#     #     [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)\
#     #     is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 \
#     #     that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""",
#     # 'logo':'https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png'},

# }

# def reset_conversation():
#     '''
#     Resets Conversation
#     '''
#     st.session_state.conversation = []
#     st.session_state.messages = []
#     return None
    


# # Define the available models
# models =[key for key in model_links.keys()]

# # Create the sidebar with the dropdown for model selection
# selected_model = st.sidebar.selectbox("Select Model", models)

# #Create a temperature slider
# temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))


# #Add reset button to clear conversation
# st.sidebar.button('Reset Chat', on_click=reset_conversation) #Reset button


# # Create model description
# st.sidebar.write(f"You're now chatting with **{selected_model}**")
# st.sidebar.markdown(model_info[selected_model]['description'])
# st.sidebar.image(model_info[selected_model]['logo'])
# # st.sidebar.markdown("*Generated content may be inaccurate or false.*")
# # st.sidebar.markdown("\nLearn how to build this chatbot [here](https://ngebodh.github.io/projects/2024-03-05/).")
# # st.sidebar.markdown("\nRun into issues? Try the [back-up](https://huggingface.co/spaces/ngebodh/SimpleChatbot-Backup).")



# if "prev_option" not in st.session_state:
#     st.session_state.prev_option = selected_model

# if st.session_state.prev_option != selected_model:
#     st.session_state.messages = []
#     # st.write(f"Changed to {selected_model}")
#     st.session_state.prev_option = selected_model
#     reset_conversation()



# #Pull in the model we want to use
# repo_id = model_links[selected_model]


# st.subheader(f'AI - {selected_model}')
# # st.title(f'ChatBot Using {selected_model}')

# # Set a default model
# if selected_model not in st.session_state:
#     st.session_state[selected_model] = model_links[selected_model] 

# # Initialize chat history
# if "messages" not in st.session_state:
#     st.session_state.messages = []


# # Display chat messages from history on app rerun
# for message in st.session_state.messages:
#     with st.chat_message(message["role"]):
#         st.markdown(message["content"])



# # Accept user input
# if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):

#     # Display user message in chat message container
#     with st.chat_message("user"):
#         st.markdown(prompt)
#     # Add user message to chat history
#     st.session_state.messages.append({"role": "user", "content": prompt})


#     # Display assistant response in chat message container
#     with st.chat_message("assistant"):
#         stream = client.chat.completions.create(
#             model=model_links[selected_model],
#             messages=[
#                 {"role": m["role"], "content": m["content"]}
#                 for m in st.session_state.messages
#             ],
#             temperature=temp_values,#0.5,
#             stream=True,
#             max_tokens=3000,
#         )

#         response = st.write_stream(stream)
#     st.session_state.messages.append({"role": "assistant", "content": response})









from huggingface_hub import InferenceClient
import gradio as gr  
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")

def format_prompt(message, history):
  prompt = "<s>"
  for user_prompt, bot_response in history:
    prompt += f"[INST] {user_prompt} [/INST]"
    prompt += f" {bot_response}</s> "
  prompt += f"[INST] {message} [/INST]"
  return prompt

def generate(
    prompt, history, temperature=0.3, max_new_tokens=3000, top_p=0.80, repetition_penalty=0.90,
):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=30,
    )

    formatted_prompt = format_prompt(prompt, history)

    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""

    for response in stream:
        output += response.token.text
        yield output
    return output
    
additional_inputs=[     
    gr.Slider(0, 1, 0.5, label="temperature"),
    gr.Slider(500, 5000, 3000, label="max_new_tokens")
    ]

#     [gr.Slider(
#         [label="temperature",
#         value=0.3,
#         minimum=0.0,
#         maximum=1.0,
#         step=0.1,
#         interactive=True,
#         info="Higher values generate more diverse outputs",]
#     ),
#     gr.Slider(
#         label="top_p",
#         value=0.3,
#         minimum=0.0,
#         maximum=1.0,
#         step=0.1,
#         interactive=True,
#         info="Higher values generate more diverse outputs",
#     ),  
# ]

    
mychatbot = gr.Chatbot(
    avatar_images=["./user.png", "./bot.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)

demo = gr.ChatInterface(fn=generate, 
                        chatbot=mychatbot,
                        title="Mistral-Chat",
                        additional_inputs=additional_inputs,
                        retry_btn=None,
                        undo_btn=None
                       )

demo.queue().launch(show_api=False)